Concurrent image and corresponding multi-channel auxiliary data generation for a generative model

Abstract

Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

What is claimed is: 
     
       1. A system, comprising:
 a memory that stores computer executable components; and 
 a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
 a multi-channel generator component that generates synthetic multichannel data associated with a synthetic version of imaging data; 
 a discriminator component that predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set; and 
 a training component that employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model; 
 
 wherein the multi-channel generator component generates a first data channel associated with a synthetic image, a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image, and a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image; and 
 wherein the computer executable components further comprise:
 a post-processing component that generates first mask data indicative of a first ground truth mask for the first segmentation data, and second mask data indicative of a second ground truth mask for the second segmentation data. 
 
 
     
     
       2. The system of  claim 1 , wherein the training component utilizes a loss function to tune the generative adversarial network model based on the first predicted class set or the second predicted class set for the synthetic multi-channel data. 
     
     
       3. The system of  claim 1 , wherein the discriminator component predicts the synthetic multi-channel data with the first predicted class set or the second predicted class set based on the first data channel associated with the synthetic image, the second data channel associated with the first segmentation data, and the third data channel associated with the second segmentation data. 
     
     
       4. The system of  claim 1 , wherein the training component tunes the generative adversarial network model based on a loss function associated with loss between first data channel, the second data channel and the third data channel. 
     
     
       5. The system of  claim 1 , wherein the computer executable components comprise:
 a synthetic-image generator component that generates the synthetic image associated with the first data channel. 
 
     
     
       6. The system of  claim 1 , wherein the computer executable components comprise:
 a segmentation component that generates the first segmentation data associated with the second data channel and the second segmentation data associated with the third data channel. 
 
     
     
       7. The system of  claim 1 , wherein the multi-channel generator component generates the synthetic multi-channel data based on a data distribution. 
     
     
       8. The system of  claim 1 , wherein the multi-channel generator component generates the synthetic multi-channel data based on a data distribution associated with a random vector. 
     
     
       9. A method, comprising using a processor operatively coupled to memory to execute computer executable components to perform the following acts:
 generating synthetic multi-channel data associated with a synthetic version of imaging data; 
 predicting multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set; and 
 training a generative adversarial network model based on the first predicted class set or the second predicted class set for the synthetic multi-channel data; 
 wherein the generating the synthetic multi-channel data comprises:
 generating a first data channel associated with a synthetic image, 
 generating a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image, and 
 generating a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image; 
 
 wherein the method further comprises: 
 generating first mask data indicative of a first ground truth mask for the first segmentation data; and 
 generating second mask data indicative of a second ground truth mask for the second segmentation data. 
 
     
     
       10. The method of  claim 9 , further comprising utilizing a loss function to tune the generative adversarial network model based on the first predicted class set or the second predicted class set for the synthetic multi-channel data. 
     
     
       11. The method of  claim 9 , wherein the predicting the multi-channel imaging data comprises predicting the synthetic multi-channel data with the first predicted class set or the second predicted class set based on the first data channel associated with the synthetic image, the second data channel associated with the first segmentation data, and the third data channel associated with the second segmentation data. 
     
     
       12. The method of  claim 9 , further comprising tuning the generative adversarial network model based on a loss function associated with loss between first data channel, the second data channel and the third data channel. 
     
     
       13. A computer readable storage device comprising instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:
 generating synthetic multi-channel data associated with a synthetic version of imaging data; 
 predicting multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set; 
 training a generative adversarial network model based on the first predicted class set or the second predicted class set for the synthetic multi-channel data; 
 wherein the generating the synthetic multi-channel data comprises:
 generating a first data channel associated with a synthetic image, 
 generating a second data channel associated with first segmentation data indicative of a segmentation for the synthetic image, and 
 generating a third data channel associated with second segmentation data indicative of a remaining segmentation for the synthetic image; 
 generating first mask data indicative of a first ground truth mask for the first segmentation data; and 
 
 generating second mask data indicative of a second ground truth mask for the second segmentation data. 
 
     
     
       14. The computer readable storage device of  claim 13 , wherein the predicting the multi-channel imaging data comprises predicting the synthetic multi-channel data with the first predicted class set or the second predicted class set based on the first data channel associated with the synthetic image, the second data channel associated with the first segmentation data, and the third data channel associated with the second segmentation data. 
     
     
       15. The computer readable storage device of  claim 13 , further comprising tuning the generative adversarial network model based on a loss function associated with loss between first data channel, the second data channel and the third data channel.